Students' Reliance on AI in Higher Education: Identifying Contributing Factors
Project Overview
The document explores the integration of generative AI in higher education, emphasizing how undergraduate students interact with AI tools. It categorizes student reliance on AI into three patterns: appropriate reliance, where helpful recommendations are correctly accepted; overreliance, where flawed suggestions are mistakenly accepted; and underreliance, where beneficial advice is incorrectly dismissed. The findings reveal that students with higher programming self-efficacy, programming literacy, and a strong need for cognition tend to demonstrate more appropriate reliance on AI tools. These insights underscore the critical need to cultivate students' critical thinking abilities and domain knowledge to minimize the risks of overreliance and enhance effective engagement with AI technologies. Overall, the study highlights the potential of generative AI to enhance educational experiences while cautioning against pitfalls associated with its misuse.
Key Applications
AI assistant providing programming problem recommendations
Context: Undergraduate students in computing-related courses
Implementation: Controlled experiment with pre- and post-surveys, and a programming task involving an AI chatbot
Outcomes: Insights into reliance patterns, factors influencing reliance, and potential interventions to promote appropriate reliance on AI
Challenges: Overreliance leading to acceptance of incorrect AI suggestions and underreliance causing rejection of helpful recommendations
Implementation Barriers
Cognitive Bias
Automation bias leads students to favor AI suggestions over their own judgment, potentially undermining learning outcomes. Lack of transparency in AI decision-making creates trust problems, making students vulnerable to overreliance.
Proposed Solutions: Educational interventions that promote critical evaluation of AI outputs and build domain expertise. Implementing cognitive forcing functions and reflective prompts to enhance critical engagement with AI.
Project Team
Griffin Pitts
Researcher
Neha Rani
Researcher
Weedguet Mildort
Researcher
Eva-Marie Cook
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Griffin Pitts, Neha Rani, Weedguet Mildort, Eva-Marie Cook
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai